Jinyu Wang
Principal Group Engineering Manager at Microsoft
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolJinyu Wang is a Principal Group Engineering Manager based in the Greater Seattle Area with six years of software engineering experience and a Master's in Computer Engineering from Syracuse University. He progressed from hands-on engineering roles at Ivalua and Microsoft to leadership at Microsoft, bringing deep C++ and C#/.NET expertise alongside practical experience with Java, C, web technologies, and MSSQL. Jinyu blends systems-level thinking (Linux/Windows/Mac) with product-focused delivery, having contributed full-stack improvements to Microsoft's open-source MARO reinforcement learning platform, including scenario-specific notebooks and API docs. Known for translating research-oriented tools into usable engineering artifacts, he pairs managerial oversight with a coder’s attention to detail and cross-platform pragmatism.
6 years of coding experience
6 years of employment as a software developer
Master's degree, Computer Engineering, Master's degree, Computer Engineering at Syracuse UniversityBachelor's degree, Automatic Control, Bachelor's degree, Automatic Control at Lanzhou Jiaotong University
English, Chinese
Github Skills (6)
jupyter-notebook10
python10
reinforcement-learning10
documentation9
api6
apidoc6
Programming languages (1)
Github contributions (5)
microsoft/maro
Sep 2020 - Jan 2023
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:18 releases, 954 reviews, 157 commits in 2 years 4 months
Contributions summary:Jinyu primarily worked on updating and refining notebook files within the `microsoft/maro` repository, which is a multi-agent reinforcement learning platform. Their commits focused on modifying the code and documentation within the notebooks, specifically for the `bike_repositioning` scenario. Additionally, the user contributed to renaming elements like "ecr" to "cim" across documentation and code, and also addressed issues with API documentation.
multi-agentreinforcement-learningmulti-agent-reinforcement-learningsimulatorresource-optimization
Jinyu-W/maro
Nov 2020 - Mar 2021
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization. It can be applied to many important industrial domains, such as container inventory management in logistics, bike repositioning in transportation, virtual machine provisioning in data centers, and asset management in finance. Besides RL, it also supports other planning/decision mechanisms, such as Operations Research. MARO provides comprehensive support in data processing, simulator building, RL algorithm selection, and distributed training.
Contributions:6 PRs, 11 pushes, 12 branches in 4 months
data-centerdistributed-trainingmulti-agentoperations-researchprovisioning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.